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README.md

InfluxDB.Client.Linq

The library supports to use a LINQ expression to query the InfluxDB.

Documentation

This section contains links to the client library documentation.

Usage

How to start

First, add the library as a dependency for your project:

# For actual version please check: https://www.nuget.org/packages/InfluxDB.Client.Linq/

dotnet add package InfluxDB.Client.Linq --version 1.17.0-dev.linq.17

Next, you should add additional using statement to your program:

using InfluxDB.Client.Linq;

The LINQ query depends on QueryApiSync, you could create an instance of QueryApiSync by:

var client = new InfluxDBClient("http://localhost:8086", "my-token");
var queryApi = client.GetQueryApiSync();

In the following examples we assume that the Sensor entity is defined as:

class Sensor
{
    [Column("sensor_id", IsTag = true)] 
    public string SensorId { get; set; }

    /// <summary>
    /// "production" or "testing"
    /// </summary>
    [Column("deployment", IsTag = true)]
    public string Deployment { get; set; }

    /// <summary>
    /// Value measured by sensor
    /// </summary>
    [Column("data")]
    public float Value { get; set; }

    [Column(IsTimestamp = true)] 
    public DateTime Timestamp { get; set; }
}

Time Series

The InfluxDB uses concept of TimeSeries - a collection of data that shares a measurement, tag set, and bucket. You always operate on each time-series, if you querying data with Flux.

Imagine that you have following data:

sensor,deployment=production,sensor_id=id-1 data=15
sensor,deployment=testing,sensor_id=id-1 data=28
sensor,deployment=testing,sensor_id=id-1 data=12
sensor,deployment=production,sensor_id=id-1 data=89

The corresponding time series are:

  • sensor,deployment=production,sensor_id=id-1
  • sensor,deployment=testing,sensor_id=id-1

If you query your data with following Flux:

from(bucket: "my-bucket")
  |> range(start: 0)
  |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
  |> drop(columns: ["_start", "_stop", "_measurement"])
  |> limit(n:1)

The result will be one item for each time-series:

sensor,deployment=production,sensor_id=id-1 data=15
sensor,deployment=testing,sensor_id=id-1 data=28

and this is also way how this LINQ driver works.

The driver supposes that you are querying over one time-series.

There is a way how to change this configuration:

Enable querying multiple time-series

var settings = new QueryableOptimizerSettings{QueryMultipleTimeSeries = true};
var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", _queryApi, settings)
    select s;

The group() function is way how to query multiple time-series and gets correct results.

The following query works correctly:

from(bucket: "my-bucket")
  |> range(start: 0)
  |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
  |> drop(columns: ["_start", "_stop", "_measurement"])
  |> group()
  |> limit(n:1)

and corresponding result:

sensor,deployment=production,sensor_id=id-1 data=15

Do not used this functionality if it is not required because it brings a performance costs caused by sorting:

Group does not guarantee sort order

The group() does not guarantee sort order of output records. To ensure data is sorted correctly, use orderby expression.

Client Side Evaluation

The library attempts to evaluate a query on the server as much as possible. The client side evaluations is required for aggregation function if there is more then one time series.

If you want to count your data with following Flux:

from(bucket: "my-bucket")
  |> range(start: 0)
  |> drop(columns: ["_start", "_stop", "_measurement"])
  |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
  |> stateCount(fn: (r) => true, column: "linq_result_column") 
  |> last(column: "linq_result_column") 
  |> keep(columns: ["linq_result_column"])

The result will be one count for each time-series:

#group,false,false,false
#datatype,string,long,long
#default,_result,,
,result,table,linq_result_column
,,0,1
,,0,1

and client has to aggregate this multiple results into one scalar value.

Operators that could cause client side evaluation:

  • Count
  • CountLong

TL;DR

Perform Query

The LINQ query requires bucket and organization as a source of data. Both of them could be name or ID.

var query = (from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    where s.SensorId == "id-1"
    where s.Value > 12
    where s.Timestamp > new DateTime(2019, 11, 16, 8, 20, 15, DateTimeKind.Utc)
    where s.Timestamp < new DateTime(2021, 01, 10, 5, 10, 0, DateTimeKind.Utc)
    orderby s.Timestamp
    select s)
    .Take(2)
    .Skip(2);

var sensors = query.ToList();

Flux Query:

from(bucket: "my-bucket") 
    |> range(start: 2019-11-16T08:20:15Z, stop: 2021-01-10T05:10:00Z) 
    |> filter(fn: (r) => (r["sensor_id"] == "id-1")) 
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
    |> drop(columns: ["_start", "_stop", "_measurement"])
    |> filter(fn: (r) => (r["data"] > 12)) 
    |> limit(n: 2, offset: 2)

Filtering

The range() and filter() are pushdown functions that allow push their data manipulation down to the underlying data source rather than storing and manipulating data in memory. Using pushdown functions at the beginning of query we greatly reduce the amount of server memory necessary to run a query.

The LINQ provider needs to aligns fields within each input table that have the same timestamp to column-wise format:

From
_time _value _measurement _field
1970-01-01T00:00:00.000000001Z 1.0 "m1" "f1"
1970-01-01T00:00:00.000000001Z 2.0 "m1" "f2"
1970-01-01T00:00:00.000000002Z 3.0 "m1" "f1"
1970-01-01T00:00:00.000000002Z 4.0 "m1" "f2"
To
_time _measurement f1 f2
1970-01-01T00:00:00.000000001Z "m1" 1.0 2.0
1970-01-01T00:00:00.000000002Z "m1" 3.0 4.0

For that reason we need to use the pivot() function. The pivot is heavy and should be used at the end of our Flux query.

There is an also possibility to disable appending pivot by:

var optimizerSettings =
    new QueryableOptimizerSettings
    {
        AlignFieldsWithPivot = false
    };
    
var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi, optimizerSettings)
    select s;

Mapping LINQ filters

For the best performance on the both side - server, LINQ provider we maps the LINQ expressions to FLUX query following way:

Filter by Timestamp

Mapped to range().

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    where s.Timestamp >= new DateTime(2019, 11, 16, 8, 20, 15, DateTimeKind.Utc)
    select s;

var sensors = query.ToList();

Flux Query:

from(bucket: "my-bucket") 
    |> range(start: 2019-11-16T08:20:15ZZ) 
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
    |> drop(columns: ["_start", "_stop", "_measurement"])

Filter by Tag

Mapped to filter() before pivot().

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    where s.SensorId == "id-1"
    select s;

Flux Query:

from(bucket: "my-bucket") 
    |> range(start: 0)
    |> filter(fn: (r) => (r["sensor_id"] == "id-1"))  
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
    |> drop(columns: ["_start", "_stop", "_measurement"])

Filter by Field

The filter by field has to be after the pivot() because we want to select all fields from pivoted table.

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    where s.Value < 28
    select s;

Flux Query:

from(bucket: "my-bucket") 
    |> range(start: 0)
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")  
    |> drop(columns: ["_start", "_stop", "_measurement"])
    |> filter(fn: (r) => (r["data"] < 28))

If we move the filter() for fields before the pivot() then we will gets wrong results:

Data
m1 f1=1,f2=2 1
m1 f1=3,f2=4 2
Without filter
from(bucket: "my-bucket") 
    |> range(start: 0)
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") 
    |> drop(columns: ["_start", "_stop", "_measurement"])

Results:

_time f1 f2
1970-01-01T00:00:00.000000001Z 1.0 2.0
1970-01-01T00:00:00.000000002Z 3.0 4.0
Filter before pivot()

filter: f1 > 0

from(bucket: "my-bucket") 
    |> range(start: 0) 
    |> filter(fn: (r) => (r["_field"] == "f1" and r["_value"] > 0))
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") 
    |> drop(columns: ["_start", "_stop", "_measurement"])

Results:

_time f1
1970-01-01T00:00:00.000000001Z 1.0
1970-01-01T00:00:00.000000002Z 3.0

Time Range Filtering

The time filtering expressions are mapped to Flux range() function. This function has start and stop parameters with following behaviour: start <= _time < stop:

Results include records with _time values greater than or equal to the specified start time and less than the specified stop time.

This means that we have to add one nanosecond to start if we want timestamp greater than and also add one nanosecond to stop if we want to timestamp lesser or equal than.

Example 1:

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    where s.Timestamp > new DateTime(2019, 11, 16, 8, 20, 15, DateTimeKind.Utc)
    where s.Timestamp < new DateTime(2021, 01, 10, 5, 10, 0, DateTimeKind.Utc)
    select s;

var sensors = query.ToList();

Flux Query:

start_shifted = int(v: time(v: "2019-11-16T08:20:15Z")) + 1

from(bucket: "my-bucket") 
    |> range(start: time(v: start_shifted), stop: 2021-01-10T05:10:00Z)
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") 
    |> drop(columns: ["_start", "_stop", "_measurement"])

Example 2:

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    where s.Timestamp >= new DateTime(2019, 11, 16, 8, 20, 15, DateTimeKind.Utc)
    where s.Timestamp <= new DateTime(2021, 01, 10, 5, 10, 0, DateTimeKind.Utc)
    select s;

var sensors = query.ToList();

Flux Query:

stop_shifted = int(v: time(v: "2021-01-10T05:10:00Z")) + 1

from(bucket: "my-bucket") 
    |> range(start: 2019-11-16T08:20:15Z, stop: time(v: stop_shifted)) 
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
    |> drop(columns: ["_start", "_stop", "_measurement"])

Example 3:

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    where s.Timestamp >= new DateTime(2019, 11, 16, 8, 20, 15, DateTimeKind.Utc)
    select s;

var sensors = query.ToList();

Flux Query:

from(bucket: "my-bucket") 
    |> range(start: 2019-11-16T08:20:15ZZ) 
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
    |> drop(columns: ["_start", "_stop", "_measurement"])

Example 4:

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    where s.Timestamp <= new DateTime(2021, 01, 10, 5, 10, 0, DateTimeKind.Utc)
    select s;

var sensors = query.ToList();

Flux Query:

stop_shifted = int(v: time(v: "2021-01-10T05:10:00Z")) + 1

from(bucket: "my-bucket") 
    |> range(start: 0, stop: time(v: stop_shifted))
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") 
    |> drop(columns: ["_start", "_stop", "_measurement"])

Example 5:

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    where s.Timestamp == new DateTime(2019, 11, 16, 8, 20, 15, DateTimeKind.Utc)
    select s;

var sensors = query.ToList();

Flux Query:

stop_shifted = int(v: time(v: "2019-11-16T08:20:15Z")) + 1

from(bucket: "my-bucket") 
    |> range(start: 2019-11-16T08:20:15Z, stop: time(v: stop_shifted)) 
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
    |> drop(columns: ["_start", "_stop", "_measurement"])

There is also a possibility to specify the default value for start and stop parameter. This is useful when you need to include data with future timestamps when no time bounds are explicitly set.

var settings = new QueryableOptimizerSettings
{
    RangeStartValue = DateTime.UtcNow.AddHours(-24),
    RangeStopValue = DateTime.UtcNow.AddHours(1)
};
var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi, settings)
    select s;

TD;LR

Supported LINQ operators

Equal

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    where s.SensorId == "id-1"
    select s;

Flux Query:

from(bucket: "my-bucket") 
    |> range(start: 0)
    |> filter(fn: (r) => (r["sensor_id"] == "id-1"))  
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
    |> drop(columns: ["_start", "_stop", "_measurement"])

Not Equal

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    where s.SensorId != "id-1"
    select s;

Flux Query:

from(bucket: "my-bucket") 
    |> range(start: 0)
    |> filter(fn: (r) => (r["sensor_id"] != "id-1")) 
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
    |> drop(columns: ["_start", "_stop", "_measurement"])

Less Than

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    where s.Value < 28
    select s;

Flux Query:

from(bucket: "my-bucket") 
    |> range(start: 0) 
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") 
    |> drop(columns: ["_start", "_stop", "_measurement"])
    |> filter(fn: (r) => (r["data"] < 28))

Less Than Or Equal

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    where s.Value <= 28
    select s;

Flux Query:

from(bucket: "my-bucket") 
    |> range(start: 0) 
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") 
    |> drop(columns: ["_start", "_stop", "_measurement"])
    |> filter(fn: (r) => (r["data"] <= 28))

Greater Than

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    where s.Value > 28
    select s;

Flux Query:

from(bucket: "my-bucket") 
    |> range(start: 0) 
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
    |> drop(columns: ["_start", "_stop", "_measurement"])
    |> filter(fn: (r) => (r["data"] > 28))

Greater Than Or Equal

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    where s.Value >= 28
    select s;

Flux Query:

from(bucket: "my-bucket") 
    |> range(start: 0) 
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") 
    |> drop(columns: ["_start", "_stop", "_measurement"])
    |> filter(fn: (r) => (r["data"] >= 28))

And

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    where s.Value >= 28 && s.SensorId != "id-1"
    select s;

Flux Query:

from(bucket: "my-bucket") 
    |> range(start: 0) 
    |> filter(fn: (r) => (r["sensor_id"] != "id-1"))
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") 
    |> drop(columns: ["_start", "_stop", "_measurement"])
    |> filter(fn: (r) => (r["data"] >= 28))

Or

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    where s.Value >= 28 || s.Value <= 5
    select s;

Flux Query:

from(bucket: "my-bucket") 
    |> range(start: 0) 
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") 
    |> drop(columns: ["_start", "_stop", "_measurement"])
    |> filter(fn: (r) => ((r["data"] >= 28) or (r["data"] <=> 28)))

Any

The following code demonstrates how to use the Any operator to determine whether a collection contains any elements. By default the InfluxDB.Client doesn't supports to store a subcollection in your DomainObject.

Imagine that you have following entities:

class SensorCustom
{
    public Guid Id { get; set; }
    
    public float Data { get; set; }
    
    public DateTimeOffset Time { get; set; }
    
    public virtual ICollection<SensorAttribute> Attributes { get; set; }
}

class SensorAttribute
{
    public string Name { get; set; }
    public string Value { get; set; }
}

To be able to store SensorCustom entity in InfluxDB and retrieve it from database you should implement IDomainObjectMapper. The converter tells to the Client how to map DomainObject into PointData and how to map FluxRecord to DomainObject.

Entity Converter:

private class SensorEntityConverter : IDomainObjectMapper
{
    //
    // Parse incoming FluxRecord to DomainObject
    //
    public T ConvertToEntity<T>(FluxRecord fluxRecord)
    {
        if (typeof(T) != typeof(SensorCustom))
        {
            throw new NotSupportedException($"This converter doesn't supports: {typeof(SensorCustom)}");
        }

        //
        // Create SensorCustom entity and parse `SeriesId`, `Value` and `Time`
        //
        var customEntity = new SensorCustom
        {
            Id = Guid.Parse(Convert.ToString(fluxRecord.GetValueByKey("series_id"))!),
            Data = Convert.ToDouble(fluxRecord.GetValueByKey("data")),
            Time = fluxRecord.GetTime().GetValueOrDefault().ToDateTimeUtc(),
            Attributes = new List<SensorAttribute>()
        };
        
        foreach (var (key, value) in fluxRecord.Values)
        {
            //
            // Parse SubCollection values
            //
            if (key.StartsWith("property_"))
            {
                var attribute = new SensorAttribute
                {
                    Name = key.Replace("property_", string.Empty), Value = Convert.ToString(value)
                };
                
                customEntity.Attributes.Add(attribute);
            }
        }

        return (T) Convert.ChangeType(customEntity, typeof(T));
    }

    //
    // Convert DomainObject into PointData
    //
    public PointData ConvertToPointData<T>(T entity, WritePrecision precision)
    {
        if (!(entity is SensorCustom ce))
        {
            throw new NotSupportedException($"This converter doesn't supports: {typeof(SensorCustom)}");
        }

        //
        // Map `SeriesId`, `Value` and `Time` to Tag, Field and Timestamp
        //
        var point = PointData
            .Measurement("custom_measurement")
            .Tag("series_id", ce.Id.ToString())
            .Field("data", ce.Data)
            .Timestamp(ce.Time, precision);

        //
        // Map subattributes to Fields
        //
        foreach (var attribute in ce.Attributes ?? new List<SensorAttribute>())
        {
            point = point.Field($"property_{attribute.Name}", attribute.Value);
        }

        return point;
    }
}

The Converter could be passed to QueryApiSync, QueryApi or WriteApi by:

// Create Converter
var converter = new SensorEntityConverter();

// Get Query and Write API
var queryApi = client.GetQueryApiSync(converter);
var writeApi = client.GetWriteApi(converter);

The LINQ provider needs to know how properties of DomainObject are stored in InfluxDB - their name and type (tag, field, timestamp).

If you use a IDomainObjectMapper instead of InfluxDB Attributes you should implement IMemberNameResolver:

private class SensorMemberResolver: IMemberNameResolver
{
    //
    // Tell to LINQ providers how is property of DomainObject mapped - Tag, Field, Timestamp, ... ?
    //
    public MemberType ResolveMemberType(MemberInfo memberInfo)
    {
        //
        // Mapping of subcollection
        //
        if (memberInfo.DeclaringType == typeof(SensorAttribute))
        {
            return memberInfo.Name switch
            {
                "Name" => MemberType.NamedField,
                "Value" => MemberType.NamedFieldValue,
                _ => MemberType.Field
            };
        }

        //
        // Mapping of "root" domain
        //
        return memberInfo.Name switch
        {
            "Time" => MemberType.Timestamp,
            "Id" => MemberType.Tag,
            _ => MemberType.Field
        };
    }

    //
    // Tell to LINQ provider how is property of DomainObject named 
    //
    public string GetColumnName(MemberInfo memberInfo)
    {
        return memberInfo.Name switch
        {
            "Id" => "series_id",
            "Data" => "data",
            _ => memberInfo.Name
        };
    }

    //
    // Tell to LINQ provider how is named property that is flattened
    //
    public string GetNamedFieldName(MemberInfo memberInfo, object value)
    {
        return "attribute_" + Convert.ToString(value);
    }
}

Now We are able to provide a required information to the LINQ provider by memberResolver parameter:

var memberResolver = new SensorMemberResolver();

var query = from s in InfluxDBQueryable<SensorCustom>.Queryable("my-bucket", "my-org", queryApi, memberResolver)
    where s.Attributes.Any(a => a.Name == "quality" && a.Value == "good")
    select s;

Flux Query:

from(bucket: "my-bucket")
    |> range(start: 0)
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") 
    |> drop(columns: ["_start", "_stop", "_measurement"])
    |> filter(fn: (r) => (r["attribute_quality"] == "good"))

For more info see CustomDomainMappingAndLinq example.

Take

var query = (from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    select s)
    .Take(10);

Flux Query:

from(bucket: "my-bucket") 
    |> range(start: 0) 
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") 
    |> drop(columns: ["_start", "_stop", "_measurement"])
    |> limit(n: 10)

Note: the limit() function can be align before pivot() function by:

var optimizerSettings =
    new QueryableOptimizerSettings
    {
        AlignLimitFunctionAfterPivot = false
    };

Performance: The pivot() is a “heavy” function. Using limit() before pivot() is much faster but works only if you have consistent data series. See #318 for more details.

TakeLast

var query = (from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    select s)
    .TakeLast(10);

Flux Query:

from(bucket: "my-bucket") 
    |> range(start: 0) 
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") 
    |> drop(columns: ["_start", "_stop", "_measurement"])
    |> tail(n: 10)

Note: the tail() function can be align before pivot() function by:

var optimizerSettings =
    new QueryableOptimizerSettings
    {
        AlignLimitFunctionAfterPivot = false
    };

Performance: The pivot() is a “heavy” function. Using tail() before pivot() is much faster but works only if you have consistent data series. See #318 for more details.

Skip

var query = (from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    select s)
    .Take(10)
    .Skip(50);

Flux Query:

from(bucket: "my-bucket") 
    |> range(start: 0) 
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") 
    |> drop(columns: ["_start", "_stop", "_measurement"])
    |> limit(n: 10, offset: 50)

OrderBy

Example 1:

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    orderby s.Deployment
    select s;

Flux Query:

from(bucket: "my-bucket") 
    |> range(start: 0) 
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") 
    |> drop(columns: ["_start", "_stop", "_measurement"])
    |> sort(columns: ["deployment"], desc: false)

Example 2:

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    orderby s.Timestamp descending 
    select s;

Flux Query:

from(bucket: "my-bucket") 
    |> range(start: 0) 
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") 
    |> drop(columns: ["_start", "_stop", "_measurement"])
    |> sort(columns: ["_time"], desc: true)

Count

Possibility of partial client side evaluation

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    select s;

var sensors = query.Count();

Flux Query:

from(bucket: "my-bucket") 
    |> range(start: 0) 
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") 
    |> drop(columns: ["_start", "_stop", "_measurement"])
    |> stateCount(fn: (r) => true, column: "linq_result_column") 
    |> last(column: "linq_result_column") 
    |> keep(columns: ["linq_result_column"])

LongCount

Possibility of partial client side evaluation

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    select s;

var sensors = query.LongCount();

Flux Query:

from(bucket: "my-bucket") 
    |> range(start: 0)
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") 
    |> drop(columns: ["_start", "_stop", "_measurement"])
    |> stateCount(fn: (r) => true, column: "linq_result_column") 
    |> last(column: "linq_result_column") 
    |> keep(columns: ["linq_result_column"])

Contains

int[] values = {15, 28};

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    where values.Contains(s.Value)
    select s;

var sensors = query.Count();

Flux Query:

from(bucket: "my-bucket")
    |> range(start: 0)
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
    |> drop(columns: ["_start", "_stop", "_measurement"])
    |> filter(fn: (r) => contains(value: r["data"], set: [15, 28]))

Custom LINQ operators

AggregateWindow

The AggregateWindow applies an aggregate function to fixed windows of time. Can be used only for a field which is defined as timestamp - [Column(IsTimestamp = true)]. For more info about aggregateWindow() function see Flux's documentation - https://docs.influxdata.com/flux/v0.x/stdlib/universe/aggregatewindow/.

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    where s.Timestamp.AggregateWindow(TimeSpan.FromSeconds(20), TimeSpan.FromSeconds(40), "mean")
    select s;

Flux Query:

from(bucket: "my-bucket") 
    |> range(start: 0) 
    |> aggregateWindow(every: 20s, period: 40s, fn: mean) 
    |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value") 
    |> drop(columns: ["_start", "_stop", "_measurement"])

Domain Converter

There is also possibility to use custom domain converter to transform data from/to your DomainObject.

Instead of following Influx attributes:

[Measurement("temperature")]
private class Temperature
{
    [Column("location", IsTag = true)] public string Location { get; set; }

    [Column("value")] public double Value { get; set; }

    [Column(IsTimestamp = true)] public DateTime Time { get; set; }
}

you could create own instance of IDomainObjectMapper and use it with QueryApiSync, QueryApi and WriteApi.

var converter = new DomainEntityConverter();
var queryApi = client.GetQueryApiSync(converter)

To satisfy LINQ Query Provider you have to implement IMemberNameResolver:

var resolver = new MemberNameResolver();

var query = from s in InfluxDBQueryable<SensorCustom>.Queryable("my-bucket", "my-org", queryApi, nameResolver)
    where s.Attributes.Any(a => a.Name == "quality" && a.Value == "good")
    select s;

for more details see Any operator and for full example see: CustomDomainMappingAndLinq.

How to debug output Flux Query

var query = (from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", _queryApi)
        where s.SensorId == "id-1"
        where s.Value > 12
        where s.Timestamp > new DateTime(2019, 11, 16, 8, 20, 15, DateTimeKind.Utc)
        where s.Timestamp < new DateTime(2021, 01, 10, 5, 10, 0, DateTimeKind.Utc)
        orderby s.Timestamp
        select s)
    .Take(2)
    .Skip(2);
    
Console.WriteLine("==== Debug LINQ Queryable Flux output ====");
var influxQuery = ((InfluxDBQueryable<Sensor>) query).ToDebugQuery();
foreach (var statement in influxQuery.Extern.Body)
{
    var os = statement as OptionStatement;
    var va = os?.Assignment as VariableAssignment;
    var name = va?.Id.Name;
    var value = va?.Init.GetType().GetProperty("Value")?.GetValue(va.Init, null);

    Console.WriteLine($"{name}={value}");
}
Console.WriteLine();
Console.WriteLine(influxQuery._Query);

How to filter by Measurement

By default, as an optimization step, Flux queries generated by LINQ will automatically drop the Start, Stop and Measurement columns:

from(bucket: "my-bucket")
  |> range(start: 0)
  |> drop(columns: ["_start", "_stop", "_measurement"])
  ...

This is because typical POCO classes do not include them:

[Measurement("temperature")]
private class Temperature
{
    [Column("location", IsTag = true)] public string Location { get; set; }
    [Column("value")] public double Value { get; set; }
    [Column(IsTimestamp = true)] public DateTime Time { get; set; }
}

It is, however, possible to utilize the Measurement column in LINQ queries by enabling it in the query optimization settings:

var optimizerSettings =
    new QueryableOptimizerSettings
    {
        DropMeasurementColumn = false,
        
        // Note we can also enable the start and stop columns
        //DropStartColumn = false,
        //DropStopColumn = false
    };

var queryable =
    new InfluxDBQueryable<InfluxPoint>("my-bucket", "my-org", queryApi, new DefaultMemberNameResolver(), optimizerSettings);

var latest =
    await queryable.Where(p => p.Measurement == "temperature")
                   .OrderByDescending(p => p.Time)
                   .ToInfluxQueryable()
                   .GetAsyncEnumerator()
                   .FirstOrDefaultAsync();

private class InfluxPoint
{
    [Column(IsMeasurement = true)] public string Measurement { get; set; }
    [Column("location", IsTag = true)] public string Location { get; set; }
    [Column("value")] public double Value { get; set; }
    [Column(IsTimestamp = true)] public DateTime Time { get; set; }
}

Asynchronous Queries

The LINQ driver also supports asynchronous querying. For asynchronous queries you have to initialize InfluxDBQueryable with asynchronous version of QueryApi and transform IQueryable<T> to IAsyncEnumerable<T>:

var client = new InfluxDBClient("http://localhost:8086", "my-token");
var queryApi = client.GetQueryApi();

var query = from s in InfluxDBQueryable<Sensor>.Queryable("my-bucket", "my-org", queryApi)
    select s;

IAsyncEnumerable<Sensor> enumerable = query
    .ToInfluxQueryable()
    .GetAsyncEnumerator();